Learning Rewrite Rules for Search Database Systems Using Query Logs

1. A method comprising: automatically populating a library of query rewrite rules utilizing a Hidden Markov Model based approach; employing a first-order Hidden Markov Model to model a user's search behavior by consulting a query log; automatically learning query rewrite rules based on data in the query log; employing an augmented Hidden Markov Model to learn via obtaining click event data at each query observation via employing a click acquisition probability metric; the click acquisition probability metric representing a probability of a click occurring when observing a query, at a given time epoch, relative to a given hidden state of an underlying Markov chain; and automatically learning query rewrite rules by making use of data in the query logs and click logs.

2. The method according to claim 1 , wherein: said learning comprises a first learning pass and a second learning pass; said first learning pass comprising obtaining data other than click event data present in the query log; said second learning pass comprising obtaining click event data present in the query log.

3. The method according to claim 2 , wherein said first learning pass comprises applying the first-order Hidden Markov Model.

4. The method according to claim 3 , wherein said first learning pass comprises defining at least one Hidden Markov Model aspect taken from the group consisting of: hidden states; observations; significance of probabilities.

5. The method according to claim 2 , wherein said first learning pass comprises obtaining requirements of learning search run-time rules via employing at least one taken from the group consisting of: query log data; manually written search runtime rules.

6. The method according to claim 2 , wherein the data other than click-through data comprise at least one taken from the group consisting of: query sequences; query session information.

7. The method according to claim 2 , wherein the click event data comprise at least one taken from the group consisting of: click-through information; analytics on click-through documents.

8. The method according to claim 2 , wherein said second learning pass comprises employing the click acquisition probability metric.

9. The method according to claim 8 , wherein: said learning comprises applying a Hidden Markov Model; wherein said applying of a Hidden Markov Model comprises employing the click acquisition probability metric.

10. The method according to claim 2 , wherein said learning further comprises combining data from said first learning pass and said second learning pass, based on probabilistic criteria.

11. The method according to claim 10 , wherein said learning further comprises developing a ranked list of inferred query rewrite rules.

12. The method according to claim 1 , further comprising: receiving a search query by a user; and consulting the query rewrite rules and thereupon reframing the search query.

13. The method according to claim 1 , wherein said learning is performed offline.

14. The method according to claim 1 , wherein the click-through data comprise historical click-through data.